Both of these works operate in the traditional paradigm of style transfer, taking texture and style to be essentially interchangeable. To truly develop algorithms that can mimic the abilities of a human artist, we need to broaden our definition of style. A preliminary effort in this direction is described in Chapter , which details our paper "Deformable Style Transfer" [1]} (joint work with Sunnie Kim). In this work we propose a framework for learning spatial warp fields which help stylize the output of other algorithms when C and S are well-paired (similar semantics and pose). Perhaps the most notable aspect of this paper is that it extends the definition of 'style' to include form and proportion. Prior work had developed domain-specific approaches to this problem for faces, but in [1]} we proposed the first domain-agnostic algorithm. The user study accompanying this paper included two major results. The first was that the warp field produced by our method dramatically improve stylization quality when combined with either STROTSS or the original neural style transfer algorithm of Gatys [3]}. The second, more interesting, finding was that the deformations produced by our method (which are often fairly substantial) do not dramatically impact perceived content preservation. This work provides evidence that form, like texture, is a rich domain to explore for improving style transfer and makes a first step towards doing so.
